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The assumptions which must be named

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A presentation on misleading simplifications and where to find them. We need to name our simplifying assumptions when modeling social scientific data, in order to avoid getting fooled by them. Slide deck available at www.mattiheino.com/naming-assumptions or https://www.slideshare.net/MatinHeino/assumptions-which-must-be-named.

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The assumptions which must be named

  1. 1. www.mattiheino.com @heinonmatti The Assumptions Which Must Be Named Social science in the post-replication crisis era Slides: mattiheino.com/naming-assumptions
  2. 2. www.mattiheino.com @heinonmatti Models and simplification Link
  3. 3. www.mattiheino.com @heinonmatti Models and simplification “All social science research must do some violence to reality in order to reveal simple truths.” - (Lazer & Friedman, 2007) Link
  4. 4. www.mattiheino.com @heinonmatti Complex adaptive systems Great intro here!
  5. 5. www.mattiheino.com @heinonmatti Complex adaptive systems Source Great intro here!
  6. 6. www.mattiheino.com @heinonmatti Great intro here! Complex adaptive systems • Emergent phenomena • Interconnectedness • Path dependence • Context specificity • Cascades • Self-organisation • Non-linear interactions • Feedback loops • Interaction-dominant causal dynamics… Source Link
  7. 7. www.mattiheino.com @heinonmatti Perceiving the world Raw reality Simplifying assumptions
  8. 8. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things Raw reality Simplifying assumptions
  9. 9. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things Raw reality Simplifying assumptions Source
  10. 10. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other Raw reality Simplifying assumptions Source
  11. 11. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging Raw reality Simplifying assumptions Source
  12. 12. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) Raw reality Simplifying assumptions Source
  13. 13. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) • Only most probable events can happen: equilibria Raw reality Simplifying assumptions Source
  14. 14. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) • Only most probable events can happen: equilibria • Consider processes as unchanging Raw reality Simplifying assumptions Source
  15. 15. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) • Only most probable events can happen: equilibria • Consider processes as unchanging • Consider components (people) as identical and unchanging Raw reality Simplifying assumptions Source
  16. 16. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) • Only most probable events can happen: equilibria • Consider processes as unchanging • Consider components (people) as identical and unchanging Raw reality Simplifying assumptions Source • Consider processes as unchanging • Consider components (people) as identical and unchanging
  17. 17. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) • Only most probable events can happen: equilibria • Consider processes as unchanging • Consider components (people) as identical and unchanging Raw reality Simplifying assumptions Source • Consider processes as unchanging • Consider components (people) as identical and unchanging
  18. 18. www.mattiheino.com @heinonmatti Ergodicity • Time average and ensemble average are equal • Measuring a single person 1000 times = measuring 1000 people 1 time • When a process is ergodic, generalising from group-level data to individuals is ok! • When non-ergodic, groups and people have different properties  Ecological fallacy Link for economists Link for (social) psychologists
  19. 19. www.mattiheino.com @heinonmatti Empirical study: • Six samples, compare within- and between individual characteristics • Positive/negative affect • Heart rate • Depressive symptoms • Means were quite close • Individuals varied wildly in time Link
  20. 20. www.mattiheino.com @heinonmatti Stationarity Between-individual relationships
  21. 21. www.mattiheino.com @heinonmatti Stationarity Between Observed average within- individual relationships
  22. 22. www.mattiheino.com @heinonmatti Stationarity ?!Between Observed average within- individual relationships
  23. 23. www.mattiheino.com @heinonmatti Stationarity: Observed time-varying relationships BetweenWithin
  24. 24. www.mattiheino.com @heinonmatti Stationarity: Observed time-varying relationships Week 1 BetweenWithin
  25. 25. www.mattiheino.com @heinonmatti Stationarity: Observed time-varying relationships Week 1 Week 10 BetweenWithin
  26. 26. www.mattiheino.com @heinonmatti Stationarity: Observed time-varying relationships Week 1 Week 10 Week 17 BetweenWithin
  27. 27. www.mattiheino.com @heinonmatti Stationarity: Observed time-varying relationships Week 1 Week 10 Week 17 Haslbeck, J. M. B., & Waldorp, L. J. (2015). MGM: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data. Link BetweenWithin
  28. 28. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) • Only most probable events can happen: equilibria • Consider processes as unchanging • Consider components (people) as identical and unchanging Raw reality Simplifying assumptions
  29. 29. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) • Only most probable events can happen: equilibria • Consider processes as unchanging • Consider components (people) as identical and unchanging Raw reality Simplifying assumptions ”What does it mean for my conclusions, if this assumption is far from reality?”
  30. 30. www.mattiheino.com @heinonmatti Perceiving the world • Draw boundaries between things • Classify elements into types • Constructs are different from each other • Consider structures as unchanging • All possible events can happen: markov processes (no memory or hysterisis) • Only most probable events can happen: equilibria • Consider processes as unchanging • Consider components (people) as identical and unchanging Raw reality Simplifying assumptions ”What does it mean for my conclusions, if this assumption is far from reality?” Link
  31. 31. www.mattiheino.com @heinonmatti Thanks! Summary: • Find out (and explicate!) the assumptions you’re making • Investigate the extent to which they violate reality • Triangulate with other models and methods when possible • Always ask: “What does it mean if I’m wrong about this?” • Next step: Learn about interaction-dominant causality Slides: mattiheino.com/naming-assumptions
  32. 32. www.mattiheino.com @heinonmatti Link Link

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